Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.13/43066
- Robust identification of process models from plant data
Goodwin, Graham C.;
Aguero, Juan C.;
Welsh, James S.;
Yuz, Juan I.;
Adams, Gregory J.;
Rojas, Cristian R.
- The University of Newcastle. Faculty of Engineering & Built Environment, School of Electrical Engineering and Computer Science
- A precursor to any advanced control solution is the step of obtaining an accurate model of the process. Suitable models can be obtained from phenomenological reasoning, analysis of plant data or a combination of both. Here, we will focus on the problem of estimating (or calibrating) models from plant data. A key goal is to achieve robust identification. By robust we mean that small errors in the hypotheses should lead to small errors in the estimated models. We argue that, in some circumstances, it is essential that special precautions, including discarding some part of the data, be taken to ensure that robustness is preserved. We present several practical case studies to illustrate the results.
- Journal of Process Control Vol. 18, Issue 9, p. 810-820
- Publisher Link
closed loop identification;
- Resource Type
- journal article